Introduction

This report accompanies The Century Foundation’s report “Exact Title TBD: Student Debt and Race in California,” which examines how student debt puts outsized financial burdens on Black and Latino families in California, especially for graduate borrowers and parents.

While California lawmakers rightly draw on national research on student debt and race, the state-specific analyses we conduct in this research can help guide state policy that accounts for the distinct patterns of borrowing in California. In particular, these analyses draw attention to the effects of uncapped Parent PLUS and Grad PLUS loans on California’s families.

In this report, we walk through the details of the data that underlie that report and explain in greater depth what we do and do not know about student debt in California. Our analysis relies on four sources:

We also draw from the Integrated Postsecondary Education Data System (IPEDS) and the American Community Survey (ACS) to lesser extents.

The structure of this data-focused report mirrors that of the policy-focused report: the charts and tables in the policy report draw from our four primary data sources in the same order that they are presented here. The first figures in the report draw from the FSA Data Center, and they are followed by figures that draw from NPSAS, and so on. This report is not a static PDF: you can toggle between different versions of charts and hover over barcharts and scatterplots to reveal data points.

This document is more descriptive than prescriptive. Even data queries did not show striking contrasts between groups are included for full transparency, so long as they are relevant to the examination of student loan debt’s burden on California borrowers.

The code used to produce this document and its charts and tables can be found at this GitHub repository. All data sets used in this report are publicly available, courtesy of the U.S. Department of Education, the U.S. Federal Reserve, and the U.S. Census Bureau.

For any questions, please email granville@tcf.org.

Federal Student Aid Data Center

About the data

The FSA Data Center is a repository of data and statistics on federal student aid, including spreadsheets on student loans reported directly from the National Student Loan Data System. For this analysis we use two files from the FSA Data Center:

  • Quarterly reports on the federal student loan portfolio by borrower location, available here.
  • Quarterly reports on Direct Loan disbursements and borrowers by program at the level of the institution, available here.

For “per capita” measures of student debt and borrowing below, the population for comparison is the estimated total of all California adults aged 18 to 50, using American Community Survey data reflecting calendar year 2021, available here.

Findings

Table 1
California’s rank on average debt measures
Measure 50-state median California value California rank
Federal student loan debt per capita $10,494 $7,973 6
Federal student loan borrowers per capita 0.298 0.215 4
Average federal student loan balance $34,623 $37,084 40
Data source and notes

California has the most outstanding federal student loan debt and borrowers of any state, amounting to around 9 percent of the total portfolio.

Table 2
Size of California borrowers’ outstanding debt
Measure U.S. total California value California share
Total outstanding federal student loan debt $1,505,800,000,000 $141,800,000,000 9.4%
Total federal student loan borrowers 41,874,000 3,824,000 9.1%
Data source and notes

Now we turn to the quarterly data on disbursements. To evade any impact from the COVID-19 pandemic on the data, we examine data from the 2018-19 award year.

Table 3
Share of loan dollars disbursed in 2018-19 by loan type, California vs. U.S.
State Subsidized Unsubsidized undergraduate Unsubsidized graduate Parent PLUS loans Grad PLUS
CA 19.0% 17.0% 31.8% 12.8% 19.4%
U.S. 21.7% 22.7% 29.8% 14.0% 11.8%
Data source and notes

Figure 1
Data source and notes

Source: FSA Data Center.

Year(s) of analysis: Reflects loans distributed in 2018-19. Filtered for four-year institutions.

The breakdown of loans distributed by California institutions is skewed more towards Grad PLUS than the breakdown of loans distributed by institutions nationwide. Unsubsidized graduate loans also take up a larger slice of the pie in California than in the nation overall.

National Postsecondary Student Aid Study

About the data

The statistics in the previous section have all been based on institution-level data. For the most robust information on the relationship between student loan borrowing and race in California, we need student-level data. In the absence of a national student-level data set, we use survey data from the National Postsecondary Student Aid Study (NPSAS).

NPSAS is the largest federal survey of U.S. college students with a primary focus on financial aid. The study has generally been conducted every four years, most recently in 2016 (NPSAS:16), with separate data sets examining undergraduate and graduate students.

NPSAS data sets have not traditionally been used for state-level analyses, although the sample size for California students can be large enough to produce reliable estimates depending on the query. This past year, a new edition of NPSAS that was designed for state-representative samples was released. Known as NPSAS-AC (Administrative Collection), it draws from student records housed by colleges and the U.S. Department of Education for a sample of 325,000 undergraduates. Representative samples for public 4-year systems are available in NPSAS-AC for 45 states, and representative samples for public 2-year systems are available for 36 states. Thirty states have representative samples for undergraduate students overall. More information on NPSAS-AC can be found here.

In this section we rely primarily on NPSAS-AC, which reflects the 2017-18 year. For analysis of graduate students, we use NPSAS:16 and filter for in-state students in California. (The in-state condition is required for this query.)

These data are accessed using the National Center on Education Statistics’ (NCES) Datalab tool. Every query has a unique table retrieval number that can be used by any user to run the query in Datalab.

Findings

The figure below compares average undergraduate borrowing by racial group at California public four-years, compared to U.S. public four-years. Across all groups, the average federal loan total is lower in California than nationwide. However, California resembles the U.S. in that Black undergraduates and their families borrow more than their peers. California differs from the U.S. in that Latino/a undergraduates in the state borrow more than white undergraduates, though this is only the case for direct loans to the students and not Parent PLUS.

Figure 2
Data source and notes

Source: NPSAS:18-AC. Table retrieval number: lounfy.

Year(s) of analysis: Reflects students enrolled in the 2017-18 academic year. Only reflects undergraduate public 4-year institutions.

Note: Insufficient sample size for Native American / Alaskan Native population. Zeros are counted in the averages, meaning it includes those who took out no loans.

Figure 3
Data source and notes

Source: NPSAS:16. Table retrieval number: psiqll. Data can be accessed at NCES Datalab.

Year(s) of analysis: Reflects students enrolled in the 2015-16 academic year. Only reflects public 4-year institutions.

Notes: Private loans are also used, but the average loans are so small that Datalab considers the estimates unreliable. Due to limitations of NPSAS:16, this only applies to in-state students.

Across all groups, graduate students in California borrow more than graduate students nationwide. Black students and those of two or more races show the highest average loans, upwards of $20,000 per year. It is striking how Black graduate students in California borrow an average that is roughly two-thirds higher than both the average for Black graduate students nationwide and the average for white graduate students in California.

Among California students, the sample is not sufficient for a breakdown by award level. For context, here is a breakdown of gaps in average loans across all graduate program levels, reflecting the national sample. This demonstrates how professional doctorates show an extreme version of the trends just described. Although professional doctoral students in California constitutes a very small subsample in NPSAS, we can explore this further using the College Scorecard later on.

Figure 4
Data source and notes

NPSAS allows us to disaggregate by ethnicity, to a limited extent, for two racial groups (Hispanic and Asian). In California, average federal loans among Filipino undergraduates are higher than other Asian groups, matching a trend seen nationwide. Among Hispanic ethnicities in California, average loans are greatest among those of Puerto Rican descent.

Figure 5: Asian ethnicities
Figure 5: Hispanic ethnicities
Data source and notes

Source: NPSAS, table retrieval number ddgpwg (U.S. all), jejvld (California in-state), tpsawz (U.S. in-state).

Note: Filtered for 4-year colleges and U.S. citizenship. Limited to in-state, undergraduate students. “Total federal loans” includes Parent PLUS. No other racial groups besides Hispanic and Asian have breakouts by ethnicity in NPSAS.

Survey of Household Economics and Decisionmaking

About the data

This is some information about SHED. Explain your process of variable selection: there are many but you chose the ones that seemed most relevant to the question of the financial burden of student loans. Emphasize here that the relationship between student loans and these variables may be chicken-and-egg, where we can’t say for sure what causes the other. Acknowledge the limitations of the sample size. Would it be nice to filter by institution level and control? Yes. Would it cut the sample size down very, very far? Yes. Small differences of one of two percentage points may be attributable to random noise that can raise from sampling. Remember to say that the respondents are heads of households, meaning that the population represented is that of U.S. adults. (Children are not included.) A good example of the kind of questions SHED answers that other datasets cannot: do borrowers think their education was worth it?

Findings

Here we tee up the findings.

Figure 6

Responses to “Do you currently have student loan debt or owe any money used to pay for your own education?”

Data source and notes

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Figure 7: Student loans

Responses to “Think about the money you currently owe for your own education. Is the money you owe for that education a student loan?”

Figure 7: Home equity

Responses to “Think about the money you currently owe for your own education. Is the money you owe for that education home equity?

Figure 7: Credit card debt

Responses to “Think about the money you currently owe for your own education. Is the money you owe for that education credit card debt?”

Data source and notes

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Figure 8: CA borrowers

Responses to “Thinking specifically about the money that you owe for your own education, please tell the total amount that you currently owe on these loans.”

Figure 8: Rest of U.S. borrowers

Responses to “Thinking specifically about the money that you owe for your own education, please tell the total amount that you currently owe on these loans.”

Data source and notes

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Figure 9: CA borrowers

Responses to “Approximately how much is the total monthly payment that you are required to make on the loans from your education?”

Figure 9: Rest of U.S. borrowers

Responses to “Approximately how much is the total monthly payment that you are required to make on the loans from your education?”

Data source and notes

Survey responses collected in 2020 and 2021 are not included due to the federal student loan payment pause in place that started in March 2020 and continued through all of 2021.
*** ### {-}

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Figure 10

Responses to “Are you behind on payments or in collections for one or more of the loans from your own education?”

Data source and notes

Survey responses collected in 2020 and 2021 are not included due to the federal student loan payment pause in place that started in March 2020 and continued through all of 2021.

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Figure 11: Debt for spouse’s or partner’s education

Responses to “Do you currently owe any money used to pay for your [spouse/ partner]’s education?”

Figure 11: Debt for child’s or grandchild’s education

Responses to “Do you currently owe any money used to pay for your child or grandchild’s education?”

Data source and notes

Figure 11 includes all respondents, not just those who have student debt for their own education. “Figure 11: Debt for spouse’s or partner’s education” does not include survey respondents without a spouse or partner. “Figure 11: Debt for child or grandchild’s education” does not include survey respondents without children or grandchildren. *** ### {-}

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Figure 12: CA borrowers

Responses to “What is the highest level of school you have completed or the highest degree you have received?”

Figure 12: Rest of U.S. borrowers

Responses to “What is the highest level of school you have completed or the highest degree you have received?”

Figure 12: CA non-borrowers

Responses to “What is the highest level of school you have completed or the highest degree you have received?”

Data source and notes

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Figure 13: CA borrowers

Responses to “Do you own your home?”

Figure 13: Rest of U.S. borrowers

Responses to “Do you own your home?”

Figure 13: CA non-borrowers

Responses to “Do you own your home?”

Data source and notes

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Figure 14: Student loan borrowers

Responses to “Do you currently have any outstanding unpaid credit card debt?”

Figure 14: Non-borrowers

Responses to “Do you currently have any outstanding unpaid credit card debt?”

Data source and notes

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Figure 15: CA borrowers

Responses to “In the past 12 months, how frequently have you carried an unpaid balance on one or more of your credit cards?”

Figure 15: Rest of U.S. borrowers

Responses to “In the past 12 months, how frequently have you carried an unpaid balance on one or more of your credit cards?”

Figure 15: CA non-borrowers

Responses to “In the past 12 months, how frequently have you carried an unpaid balance on one or more of your credit cards?”

Data source and notes

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Figure 16: CA borrowers

Responses to “What is the approximate total amount of your household’s savings and investments?”

Figure 16: Rest of U.S. borrowers

Responses to “What is the approximate total amount of your household’s savings and investments?”

Figure 16: CA non-borrowers

Responses to “What is the approximate total amount of your household’s savings and investments?”

Data source and notes

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Figure 17: CA borrowers

Responses to “Which of the following categories best describes the total income that you received from all sources, before taxes and deductions, in the past 12 months?”

Figure 17: Rest of U.S. borrowers

Responses to “Which of the following categories best describes the total income that you received from all sources, before taxes and deductions, in the past 12 months?”

Figure 17: CA non-borrowers

Responses to “Which of the following categories best describes the total income that you received from all sources, before taxes and deductions, in the past 12 months?”

Data source and notes

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Figure 18: CA borrowers

Responses to “Overall, which one of the following best describes how well you are managing financially these days?”

Figure 18: Rest of U.S. borrowers

Responses to “Overall, which one of the following best describes how well you are managing financially these days?”

Figure 18: CA non-borrowers

Responses to “Overall, which one of the following best describes how well you are managing financially these days?”

Data source and notes

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Figure 19: CA borrowers

Responses to “Think of your parents when they were your age. Would you say you (and your family) are better, the same, or worse off financially than they were?”

Figure 19: Rest of U.S. borrowers

Responses to “Think of your parents when they were your age. Would you say you (and your family) are better, the same, or worse off financially than they were?”

Figure 19: CA non-borrowers

Responses to “Think of your parents when they were your age. Would you say you (and your family) are better, the same, or worse off financially than they were?”

Data source and notes

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Figure 20: CA borrowers

Responses to “Where do you think your credit score falls?”

Figure 20: Rest of U.S. borrowers

Responses to “Where do you think your credit score falls?”

Figure 20: Non-borrowers

Responses to “Where do you think your credit score falls?”

Data source and notes

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Figure 21: CA borrowers

Responses to “Which best describes your ability to pay all of your bills in full this month?”

Figure 21: Rest of U.S. borrowers

Responses to “Which best describes your ability to pay all of your bills in full this month?”

Figure 21: CA non-borrowers

Responses to “Which best describes your ability to pay all of your bills in full this month?”

Data source and notes

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Figure 22: Ability to pay student loan bill

Responses to “Are you expecting to be unable to pay or only make a partial payment on your student loan bill this month?”

Figure 22: Ability to pay rent or mortgage bill

Responses to “Are you expecting to be unable to pay or only make a partial payment on each of the following bills this month?”

Figure 22: Ability to pay credit card bill

Responses to

Data source and notes

Survey years 2020 and 2021 not included in “Figure 22: Ability to pay student loan bill” due to the federal student loan repayment pause. ***

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Figure 23

Responses to “Would you likely skip paying, or make only a partial payment on, your student loan bill if you had a $400 emergency expense that you had to pay?”

Data source and notes

Limited to those who have student loans. Survey years 2020 and 2021 not included. *** ### {-}

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Figure 24: CA borrowers

Responses to “How would a $400 emergency expense that you had to pay impact your ability to pay your other bills this month?”

Figure 24: Rest of U.S. borrowers

Responses to “How would a $400 emergency expense that you had to pay impact your ability to pay your other bills this month?”

Figure 24: CA non-borrowers

Responses to “How would a $400 emergency expense that you had to pay impact your ability to pay your other bills this month?”

Data source and notes

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Figure 25: CA borrowers

Responses to “If you were to apply for a credit card today, how confident are you that you would be approved?”

Figure 25: Rest of U.S. borrowers

Responses to “If you were to apply for a credit card today, how confident are you that you would be approved?”

Figure 25: CA non-borrowers

Responses to “If you were to apply for a credit card today, how confident are you that you would be approved?”

Data source and notes

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Figure 26

Responses to “In the past 12 months, have you or your spouse/partner been turned down for credit?”

Data source and notes

Add a lot of caveats before this next section.

Figure 27: Social Security

Responses to “In the past 12 months, have you [and/or your spouse/ parnter] received Social Security (including old age and DI)?”

Figure 27: Cash assistance

Responses to “In the past 12 months, have you [and/or your spouse/ parnter] received Supplemental Security Income (SSI), TANF, or cash assistance from a welfare program?”

Figure 27: EITC

Responses to “In the past 12 months, have you [and/or your spouse/ parnter] received the Earned Income Tax Credit (EITC)?”

Figure 27: SNAP

Responses to “In the past 12 months, have you [and/or your spouse/ parnter] received Supplemental Nutrition Assistance Program (SNAP or food stamps)?”

Figure 27: Housing assistance

Responses to “In the past 12 months, have you [and/or your spouse/ parnter] received housing assistance from a government program?”

Figure 27: FRPL

Responses to “In the past 12 months, have you [and/or your spouse/ parnter] received free or reduced price school lunches?”

Data source and notes

It’s weird that the “Cover expenses with savings” bars are lower than the “Cover expenses by any means” bars.

Figure 28: Cover expenses with savings

Responses to “Have you set aside emergency or rainy day funds that would cover your expenses for 3 months in case of sickness, job loss, economic downturn, or other emergencies?”

Figure 28: Cover expenses by any means

Responses to “If you were to lose your main source of income (for example, job or government benefits), could you cover your expenses for 3 months by borrowing money, using savings, or selling assets?”

Data source and notes

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Figure 29: Pay $400 with credit card (no interest)

Responses to “Suppose that you have an emergency expense that costs $400. Based on your current financial situation, how would you pay for this expense? [Put it on my credit card and pay it off in full at the next statement]”

Figure 29: Pay $400 with credit card (with interest)

Responses to “Suppose that you have an emergency expense that costs $400. Based on your current financial situation, how would you pay for this expense? [Put it on my credit card and pay it off over time]”

Figure 29: Pay $400 with cash or savings

Responses to “Suppose that you have an emergency expense that costs $400. Based on your current financial situation, how would you pay for this expense? [With the money currently in my checking/savings account or with cash]”

Figure 29: Pay $400 with a bank loan

Responses to “Suppose that you have an emergency expense that costs $400. Based on your current financial situation, how would you pay for this expense? [Using money from a bank loan or line of credit]”

Figure 29: Pay $400 by borrowing from a friend

Responses to “Suppose that you have an emergency expense that costs $400. Based on your current financial situation, how would you pay for this expense? [By borrowing from a friend or family member]”

Figure 29: Pay $400 with a payday loan

Responses to “Suppose that you have an emergency expense that costs $400. Based on your current financial situation, how would you pay for this expense? [Using a payday loan, deposit advance, or overdraft]”

Figure 29: Pay $400 by selling something

Responses to “Suppose that you have an emergency expense that costs $400. Based on your current financial situation, how would you pay for this expense? [By selling something]”

Figure 29: Unable to pay $400

Responses to “Suppose that you have an emergency expense that costs $400. Based on your current financial situation, how would you pay for this expense? [I wouldn’t be able to pay for the expense right now]”

Data source and notes

Responses are not exclusive to each other. For an example, a respondent could say that they would sell belongings and use a payday loan. Because of this, groups’ bars cannot be stacked on top of each other in one chart and are instead presented here in a series of charts. *** ### {-}

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Figure 30: CA borrowers

Responses to “Overall, how would you say the lifetime financial benefits of your [current/most recent] educational program compare to its financial costs?”

Figure 30: Rest of U.S. borrowers

Responses to “Overall, how would you say the lifetime financial benefits of your [current/most recent] educational program compare to its financial costs?”

Figure 30: CA non-borrowers

Responses to “Overall, how would you say the lifetime financial benefits of your [current/most recent] educational program compare to its financial costs?”

Data source and notes

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Figure 31: Different field

Responses to “If you could go back and make your education decisions again, would you have chosen a different field of study?”

Figure 31: Different school

Responses to “If you could go back and make your education decisions again, would you have attended a different school?”

Figure 31: Less education

Responses to “If you could go back and make your education decisions again, would have not attended college or completed less education?”

Figure 31: More education

Responses to “If you could go back and make your education decisions again, would you have completed more education?”

Data source and notes

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Figure 32: CA borrowers

Responses to

Figure 32: Rest of U.S. borrowers

Responses to

Figure 32: CA non-borrowers

Responses to

Data source and notes

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Figure 33: CA borrowers

Responses to

Figure 33: Rest of U.S. borrowers

Responses to

Figure 33: CA non-borrowers

Responses to

Data source and notes

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College Scorecard

Remaining Gaps